Why We Need AI
by Zoltanous
In Towards a New Socialism, Paul Cockshott presents a compelling vision of how cybernetic economic planning and classical econophysics can guide society toward a socialist future, leveraging the disruptive power of artificial intelligence (AI) and technological innovation. By challenging conventional views on production, ownership, and particularly intellectual property, Cockshott's work critically examines the capitalist framework, advocating for a system where ideas and creative works are shared communal resources rather than privately owned assets. This approach not only questions the capitalist valuation of digital and creative works but also emphasizes how early-stage scientific fields can magnify the influence of personal subjectivities and the specifics of their development context. Cockshott's analysis extends to the defense of private property, traditionally justified as the fruit of individual labor. With AI's emergence threatening to automate creative jobs, the discourse echoes concerns reminiscent of the industrial revolution. This situation calls for a reevaluation of socialist principles to address technological advances and protect workers' rights across sectors. The critique that AI exacerbates capitalist inequalities overlooks the dialectical process, which sees technological advancements like AI as a means to transcend capitalism by undermining its foundational structures and fostering a more collective approach to production.
The resistance to AI, particularly from those within the creative industries who rely on intellectual property for their livelihood, is often viewed as an attempt to maintain outdated modes of production. However, this view neglects the potential of AI to democratize content creation and diversify media representation, challenging existing power dynamics within the media industry and beyond. Despite fears of job displacement and cultural degradation, AI's integration into artistic and cultural production can enrich the creative landscape, making art more accessible and reflective of a broader range of human experience. Furthermore, the opposition to AI from certain quarters of the art world, which aligns with corporate interests, underscores a broader struggle for control over cultural production and representation. While concerns about AI-enhanced surveillance and the loss of artistic authenticity persist, it's essential to recognize that technology can also serve as a powerful tool for expression and innovation, breaking down barriers that have historically restricted access to creative endeavors.
The integration of AI into the arts is not about diminishing traditional art forms or curbing creativity but about opening up new avenues for engaging with and reinterpreting cultural expressions. The advent of digital mediums has not led to the obsolescence of traditional art; instead, these forms continue to flourish. While concerns regarding AI's impact on the livelihoods of artists and the authenticity of creative work are valid, they should be weighed against ethical considerations, such as the environmental footprint of digital technologies, and an understanding that the value of art lies in its ability to resonate with audiences. The primary concern with AI in art revolves around its potential misuse in fraud. Nonetheless, existing legal mechanisms for tackling issues like defamation and impersonation can be expanded to encompass AI-related challenges, with the development of secure digital identities offering a form of mitigation.
The challenges posed by AI are not entirely unprecedented but rather extend ongoing legal and ethical discussions. The emergence of AI brings with it both risks and the opportunity for society to adopt a more discerning approach to visual information, possibly leading to a greater appreciation for written content and analysis due to the increasing unreliability of imagery. This shift could move us away from profit-centric motives towards a community-oriented, information-based economy, challenging the foundations of our current capitalist structure. AI presents a chance to align technological advancement with human welfare, envisioning a future where the benefits of AI are shared widely, especially among workers, and prompting a reevaluation of societal norms and the role of technology and capitalism.
From a technical standpoint, the economic and environmental costs associated with computational processes, ranging from manual calculations to sophisticated computing, are considerable. The field of computer science is dedicated to refining these processes, striving for efficiency and manageable complexity in problem-solving. This endeavor not only advances computer science but also sheds light on economic models, viewing them as systems that optimize the distribution of resources. Despite the inherent challenges in both market and centrally planned economies, advancements in computing technology hold the promise for more effective management systems, provided computational limitations can be addressed. Leonid Kantorovich's proposition to employ linear programming for resource allocation underscores the theoretical feasibility but also highlights the practical difficulties, such as ensuring data accuracy and meeting computational demands, that make comprehensive economic planning a formidable task.
To enable timely and cost-efficient analysis, a novel digital model using a search algorithm for resource distribution has been proposed. This model, which opts for an Input/Output matrix to emphasize data sparsity, makes the economic planning process more manageable by simplifying how the economy's components are represented. It is more efficient in terms of computation and storage than traditional linear programming approaches. Drawing inspiration from neural networks, the model views industries as interconnected nodes, akin to neurons, to map out their interrelations. The goal is to 'train' the model to optimize resource distribution effectively, guided by a Harmony function that evaluates how well economic output aligns with societal goals, prioritizing balance and efficiency.
The resource allocation process involves several steps:
Identifying each industry's bottleneck.
Optimizing inputs and reallocating excess.
Assessing production alignment with a harmony function.
Calculating and prioritizing industries by harmony.
Redistributing resources to improve balance.
Iteratively refining to enhance economic harmony.
This innovative approach to resource distribution, designed to be within the capabilities of current computing technology, has undergone experimental validation through simulations that mimic various economic conditions. Written in the C programming language, the algorithm has shown it can feasibly and efficiently produce the desired outcomes and surplus production, aligning with Sraffa's criteria across randomized economic scenarios. The algorithm successfully enhances resource distribution among industries, promoting a more balanced economy, though it occasionally leaves some resources unused. This suggests it might prioritize achieving a general state of harmony over maximizing resource utility.
Addressing this issue and to steer clear of the local optima — suboptimal points where the algorithm might prematurely converge — such as incorporating simulated annealing techniques, including an amplification factor and the introduction of random noise. These adjustments are designed to help the system navigate past these local maxima. Interestingly, in Monte Carlo simulations that compared three variations of the algorithm, only the introduction of the amplification factor significantly improved the system's harmony. This outcome indicates that while the amplification factor is effective in bypassing local peaks, the addition of random noise does not markedly influence the results. The algorithm's precision was further confirmed through a validation against analytical methods in a controlled setup, where its results closely matched the theoretical ideal, showcasing its high level of accuracy. Analysis of its performance revealed that computation time increases linearly with the number of industries involved, which validates the algorithm's efficiency and scalability in optimizing resource distribution across a wide range of industries.
Experimental testing has demonstrated that an algorithm's capability to efficiently manage resources across thousands of industries using modest computing resources, suggests that has an applicability to large-scale economic planning. The feasibility of applying this computational model on a grand scale is further supported by the potential of modern supercomputers to manage resource distribution across major economies. The success of this computational strategy, however, hinges on the access to and accuracy of real-time economic data. Fortunately, with the ongoing advancements in microcomputing and telecommunications, the prospect of establishing a network for the continuous collection and updating of economic data is increasingly realistic. This development positions computer-assisted AI economic planning as a promising alternative to conventional economic management, potentially transforming how we approach economic planning and optimization.
AI and Automation are going to worthy areas of study for us in the foreseeable future. The Soviet OGAS and Chilean Cybersyn experiments should be studied. The development and implementation of such technology in America should be brought in the imagination. The necessary reindustrialization of the United States, even from the context of Lyndon LaRouche's Four Laws for economy recovery, should include an automation project on the scale the USSR proposed for OGAS during the 1960s. The automation project can be embedded in LaRouche's Fourth Law; which concerns science drivers as the engine of economic development or recovery, alongside fusion energy and space exploration.